Skill UI

This sophisticated skill is designed to locate the root cause of a confirmed software failure. It performs a systematic, semi-formal tracing process: first tracing backward from the observed failure point (using provided stack traces, assertions, or error messages) to identify the original premise, and then tracing forward to confirm the symptom. It outputs a highly structured report detailing the divergence, premises, and remedy.

This model provides an engineering operational framework for teams that leverage AI agents to generate large volumes of code. It shifts the focus from simple syntax to process quality, robust architecture design, rigorous testing, and systematic risk control, ensuring the development of highly stable and reliable systems in AI-assisted environments.

Orchestrates bootstrapping a working MVP from a design or spec document: ingest the SDD/PRD, plan thin vertical slices, scaffold the first end-to-end slice, then drive a generator-evaluator build loop with review gates and gated feat commits.

This tool is designed to manage the process of altering an existing, functional feature to meet a new desired specification. It ensures a structured workflow: first, update the existing test cases to reflect the new behavior; second, modify the underlying implementation to pass these updated tests; finally, complete through review and gated commit. Use this when the feature works but its behavior needs adjustment.

A comprehensive guide for structuring the related work section in interdisciplinary HRI papers. It teaches authors how to simultaneously situate their research across multiple literatures—including robotics, HCI, psychology, and design—while employing a 'delta-first' argument to clearly articulate novelty and maintaining double-blind review compliance.

This guide provides a comprehensive second-pass capability check for academic manuscripts, designed to meet the rigorous standards of top-tier econometric journals like the Journal of Econometrics (JoE). It verifies abstract conciseness, structures the introduction contribution-first, improves the legibility of complex proofs, and ensures adherence to Elsevier/elsarticle formatting conventions.

This skill processes raw, report-only optimization observations generated by Caveman tools. It guides the user through an evidence-first, rigorous evaluation process, requiring explicit operator choice, designing a minimal candidate change, and performing a paired baseline evaluation using identical fixed inputs. It ensures that any proposed code change is validated against quality checks and established performance metrics before implementation.

This skill applies Refactoring.Guru-inspired rules for diagnosing code smells and selecting appropriate refactoring techniques. It helps preserve behavior while avoiding uncontrolled redesign. Before making design or code decisions, read the mini rules first. Use the full guide only as a deeper reference when needed. It ensures targeted cleanup without over-engineering.

This skill orchestrates full-stack feature development across backend, frontend, and infrastructure layers using an API-first approach. It guides users through architecture design, parallel implementation, and integration testing phases, ensuring cohesive system delivery with modern best practices and verification steps.

This guide provides practical implementation steps for ensuring GDPR compliance in data processing, consent management, and privacy controls. Use this skill when developing systems that handle EU personal data, managing Data Subject Requests (DSRs), or designing privacy-first architectures to meet strict international data regulations.

Invariant-Guard enforces a correctness-first approach to algorithm design. Instead of writing code and hoping it works, this skill mandates defining the function contract, loop invariants, termination arguments, and all potential edge cases *before* implementation. It is essential for complex algorithms where subtle bugs (like off-by-one errors or missed post-conditions) are common, ensuring mathematically rigorous and verifiable code.

Build and deploy the same feature consistently across web, iOS, Android, and desktop using API-first architecture, shared design systems, and parallel implementation strategies to achieve cross-platform feature parity.